Skip to main content

Advertisement

SpringerLink
Log in

Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR scheme (TSCBIR) for computer-aided diagnosis of lung nodules. Two similarity metrics, semantic relevance and visual similarity, are introduced to measure the similarity of different nodules. The first step is to search for K most similar reference ROIs for each queried ROI with the semantic relevance metric. The second step is to weight each retrieved ROI based on its visual similarity to the queried ROI. The probability is computed to predict the likelihood of the queried ROI depicting a malignant lesion. In order to verify the feasibility of the proposed algorithm, a lung nodule dataset including 366 nodule regions of interest (ROIs) is assembled from LIDC-IDRI lung images on CT scans. Three groups of texture features are implemented to represent a nodule ROI. Our experimental results on the assembled lung nodule dataset show good performance improvement over existing popular classifiers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Siegel, R.L., Miller, K.D., and Jemal, A., Cancer statistics, 2015. CA Cancer J Clin. 65:5–29, 2015. https://doi.org/10.3322/caac.21254.

    Article  PubMed  Google Scholar 

  2. Armato, R.S., Mclennan, G., Bidaut, L., et al., The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 38(2):915–931, 2011. https://doi.org/10.1118/1.3528204.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Huang, P., Park, S., Yan, R., et al., Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: a matched case-control study. Radiology. 5:162725, 2017. https://doi.org/10.1148/radiol.2017162725.

    Google Scholar 

  4. Lu, C., Zhu, Z., and Gu, X., An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method. J Med Syst. 38(9):97–105, 2014. https://doi.org/10.1007/s10916-014-0097-y.

    Article  PubMed  Google Scholar 

  5. Orozco, H., Villegas, O., Sánchez, V., Domínguez, H., and Alfaro, M., Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. BioMed Eng. 14(1):9, 2015. https://doi.org/10.1186/s12938-015-0003-y.

    Google Scholar 

  6. Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., Zang, Y., and Tian, J., Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn. 61:663–673, 2017. https://doi.org/10.1016/j.patcog.2016.05.029.

    Article  Google Scholar 

  7. Kumar, A., Kim, J., Cai, W., Fulham, M., and Feng, D., Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J Digit Imaging. 26:1025–1039, 2013. https://doi.org/10.1007/s10278-013-9619-2.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gundreddy, R.R., Tan, M., Qiu, Y., Cheng, Liu, H., and Zheng, B., Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions. Med Phys. 42:4241–4249, 2015. https://doi.org/10.1118/1.4922681.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Jiang, M., Zhang, S., Li, H., and Metaxas, D.N., Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE T Biomed Eng. 62(2):783–791, 2015. https://doi.org/10.1109/TBME.2014.2365494.

    Article  Google Scholar 

  10. Tsochatzidis, L., Zagoris, K., Arikidis, N., et al., Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recogn. 71:106–117, 2017. https://doi.org/10.1016/j.patcog.2017.05.023.

    Article  Google Scholar 

  11. Dubey, S.R., Singh, S.K., and Singh, R.K., Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases. IEEE T Image Process. 24(12):5892–5903, 2015. https://doi.org/10.1109/TIP.2015.2493446.

    Article  Google Scholar 

  12. Ma, L., Liu, X., Gao, Y., et al., A new method of content based medical image retrieval and its applications to CT imaging sign retrieval. J Biomed Inform. 66:148–158, 2017. https://doi.org/10.1016/j.jbi.2017.01.002.

    Article  PubMed  Google Scholar 

  13. Liu, Y., Jin, R., Lily, M., Sukthankar, R., Goode, A., Zheng, B., Hoi, S.C.H., and Satyanarayanan, M., A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. IEEE T Pattern Anal. 32:30–44, 2010. https://doi.org/10.1109/tpami.2008.273.

    Article  Google Scholar 

  14. Wei, G., Ma, H., Qian, W., and Qiu, M., Similarity measurement of lung masses for medical image retrieval using kernel based semisupervised distance metric. Med Phys. 43(12):6259–6269, 2016. https://doi.org/10.1118/1.4966030.

    Article  PubMed  Google Scholar 

  15. Armato, S.G., et al., The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 38(2):915–931, 2011. https://doi.org/10.1118/1.3528204.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Warfield, S.K., Zou, K.H., and Wells, W.M., Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE T Med Imaging. 23(7):903–921, 2004. https://doi.org/10.1109/TMI.2004.828354.

    Article  Google Scholar 

  17. Ojala, T., Pietikäinen, M., and Mäenpää, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 24(7):971–987, 2002. https://doi.org/10.1109/tpami.2002.1017623.

    Article  Google Scholar 

  18. Wei, C. H., Li, Y., and Li, C. T., Effective extraction of Gabor features for adaptive mammogram retrieval. in 2007 I.E. International Conference on Multimedia and Expo (IEEE, New York, NY, 2007), pp. 1503–1506.

  19. Haralick, R.M., Shanmugam, K., and Dinstein, I., Textural features for image classification. IEEE Trans Syst Man Cybern. 6:610–621, 1973.

    Article  Google Scholar 

  20. Sluimer, I.C., van Waes, P.F., Viergever, M.A., and Ginneken, B.v., Computer-aided diagnosis in high resolution CT of the lungs. Med Phys. 30(12):3081–3090, 2003. https://doi.org/10.1118/1.1624771.

    Article  PubMed  Google Scholar 

  21. Liu, Y., and Jin, R., Distance metric learning: a comprehensive survey, technical report. Report No. UCB/CSD-02-1206, 2006.

  22. Xiong, Y., Luo, Y., Huang, W., Zhang, W., Yang, Y., and Gao, J., A novel classification method based on ICA and ELM: a case study in lie detection. Bio-Med Mater Eng. 24(1):357–363, 2014. https://doi.org/10.3233/BME-130818.

    Google Scholar 

Download references

Acknowledgements

The research is supported by the Recruitment Program of Global Experts (Grant no. 01270021814101/022), the National Natural Science Foundation of China (No. 61702087; No. 81671773; No. 61672146; No. 81473708), the Fundamental Research Funds for the Central Universities (Grant no. N150408001).

Author information

Authors and Affiliations

  1. School of Science and Engineering, Shandong University of Traditional Chinese medicine, Jinan, 250355, China

    Guohui Wei, Hui Cao & Zhiqing Ma

  2. Sino-Dutch Biomedical and Information Engineering School, Northeast University, Shenyang, China

    He Ma, Shouliang Qi & Wei Qian

  3. Department of Electrical and Computer Engineering, University of Texas, El Paso, TX, USA

    Wei Qian

Authors
  1. Guohui Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. He Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shouliang Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhiqing Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guohui Wei.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, G., Cao, H., Ma, H. et al. Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric. J Med Syst 42, 13 (2018). https://doi.org/10.1007/s10916-017-0874-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-017-0874-5

Keywords

Search

Navigation